Which distributional cues help the most? Unsupervised contexts selection for lexical category acquisition
|Title||Which distributional cues help the most? Unsupervised contexts selection for lexical category acquisition|
|Publication Type||Conference Proceedings|
|Year of Publication||2015|
|Authors||Cassani, G., Grimm R., Daelemans W., & Gillis S.|
|Conference Name||Sixth Workshop on Cognitive Aspects of Computational Language Learning (CogACLL 2015), Lisbon, Portugal|
|Keywords||computational psycholinguistics, Distributional bootstrapping, frame-based approaches, language acquisition, Lexical categories induction|
Starting from the distributional bootstrapping hypothesis, we propose an unsupervised model that selects the most useful distributional information according to its salience in the input, incorporating psycholinguistic evidence. With a supervised Parts-of-Speech tagging experiment, we provide preliminary results suggesting that the distributional contexts extracted by our model yield similar performances as compared to current approaches from the literature, with a gain in psychological plausibility. We also introduce a more principled way to evaluate the effectiveness of distributional contexts in helping learners to group words in syntactic categories.